A NON-PARAMETRIC APPROACH TO SELECTION BIAS: EXAMINING THE ORGANIZATIONAL EFFECT OF FAMILY MEDICINE GROUPS ON ACCESS TO PRIMARY CARE AMONG DIABETICS IN QUEBEC
Bibliographic record
Abstract
Introduction Within Canada, Quebecers experience the greatest barriers in accessing primary care with over 25% of the population without a regular family physician and the highest rate of emergency department (ED) use. Family Medicine Groups (FMGs) were introduced in 2002 to provide greater access to care, namely for individuals with chronic conditions such as diabetes. Some key characteristics of the FMG organizational reform are: (1) it is based on voluntary physician take-up; (2) its regional variation in implementation; (3) its gradual deployment across the province. These factors may produce selection bias between early and late physician implementers. Objectives To determine whether the regional rate of avoidable ED visits among diabetics co-varies with FMG wave of implementation distinguishing between early and late physician adopters of the model. Methods An ecological analysis will be conducted to meet this objective. The outcome is access to primary care measured by the regional rate of avoidable ED visits among diabetics between 2003 and 2012. Regions are defined on an urban to rural continuum according to each administrative region's distance from the province's main city centers, Montreal and Quebec City. The study will be conducted using linked administrative health databases for which access has already been granted. Non-parametric spline regression will be used to distinguish between waves of FMG implementation. The spline knots, denoting a significant change in the rate of avoidable ED visits, will be estimated from the data. To improve internal validity, the analysis will incorporate a control series examining trauma visits to the ED that should not be affected by the FMG reform. Results The analyses are in progress and results are expected by the end of May. Conclusion This study will be the first to empirically define waves of FMG implementation and their effects on access to primary care among diabetics. These findings will inform future studies examining access to primary care at an individual patient level where failure to control for FMG waves may produce biased estimates of the reform's organizational effect.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.115 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".